Deep learning has become a transformative technology in ophthalmology, significantly enhancing the identification, categorization, and treatment of numerous eye conditions. This review explores its applications in diagnosing ailments such as diabetic retinopathy, glaucoma, cataracts, age-related macular degeneration, and other retinal disorders. The incorporation of sophisticated artificial intelligence, particularly AI-driven models integrated with convolutional neural networks, has markedly improved the precision and effectiveness of imaging methods like optical coherence tomography and fundus photography for disease detection. Recent innovations in transfer learning, ensemble approaches, and hybrid deep learning frameworks have further improved diagnostic accuracy, reduced false positives, and enabled early disease identification. Deep learning models can extract complex features from retinal images, which will allow for automated, scalable, and cost-effective screening solutions, especially for under-resourced healthcare environments. In addition, the integration of explainable AI methods is improving model transparency, leading to increased confidence among healthcare professionals and patients. These advancements notwithstanding, the challenges still abound in data privacy, model generalization, and the need for large and heterogeneous datasets to avoid biases.